SGLP Working Draft
Eugene Chong
July 08, 2020
Admin
Load packages
source("~scripts/00 - Admin.R")
source("~scripts/01 - Utility Functions.R")Read Data
Siegel data for all cities
source("~scripts/10 - Read Siegel Data.R")## Warning: Missing column names filled in: 'X1' [1]Gun incidents for all cities
# source("~scripts/11 - Read crime data.R")
guns_df <- readRDS("~outputs/10/11_guns_df.rds")# source("~scripts/21 - Clean crime data.R")
guns_clean <- readRDS("~outputs/20/21_guns_clean.rds")
guns_list <- readRDS("~outputs/20/21_guns_list.rds")Sample of data for testing
set.seed(1)
guns_sample_df <- sample_n(guns_df, 10000)
guns_sample_ls <- guns_sample_df %>%
split(., # split into nested list of dfs by city
f = .$city)Census data for all cities
# source("~scripts/12 - Read census data.R")Explore Data
Siegel
Date range: 1991-2019
range(siegel_raw$year)## [1] 1991 2019134 different laws…
unique(siegel_raw$law)## [1] age18longgunpossess age18longgunsale
## [3] age21handgunpossess age21handgunsale
## [5] age21longgunpossess age21longgunsale
## [7] age21longgunsaled alcoholism
## [9] alctreatment amm18
## [11] amm21h ammbackground
## [13] ammlicense ammpermit
## [15] ammrecords ammrestrict
## [17] assault assaultlist
## [19] assaultregister assaulttransfer
## [21] backgroundpurge cap14
## [23] cap16 cap18
## [25] capaccess capliability
## [27] capunloaded capuses
## [29] ccbackground ccbackgroundnics
## [31] ccrenewbackground ccrevoke
## [33] college collegeconcealed
## [35] danger dealer
## [37] dealerh defactoreg
## [39] defactoregh drugmisdemeanor
## [41] dvro dvrodating
## [43] dvroremoval dvrosurrender
## [45] dvrosurrenderdating dvrosurrendernoconditions
## [47] elementary exparte
## [49] expartedating expartesurrender
## [51] expartesurrenderdating expartesurrendernoconditions
## [53] felony fingerprint
## [55] gunshow gunshowh
## [57] gvro gvrolawenforcement
## [59] immunity incidentall
## [61] incidentremoval inspection
## [63] invcommitment invoutpatient
## [65] junkgun liability
## [67] lockd locked
## [69] lockp lockstandards
## [71] loststolen magazine
## [73] magazinepreowned mayissue
## [75] mcdv mcdvdating
## [77] mcdvremovalallowed mcdvremovalrequired
## [79] mcdvsurrender mcdvsurrenderdating
## [81] mcdvsurrendernoconditions mentalhealth
## [83] microstamp nosyg
## [85] onefeature onepermonth
## [87] opencarryh opencarryl
## [89] opencarrypermith opencarrypermitl
## [91] permit permitconcealed
## [93] permith permitlaw
## [95] personalized preemption
## [97] preemptionbroad preemptionnarrow
## [99] purge recordsall
## [101] recordsallh recordsdealer
## [103] recordsdealerh registration
## [105] registrationh relinquishment
## [107] reportall reportallh
## [109] reportdealer reportdealerh
## [111] residential security
## [113] showing stalking
## [115] statechecks statechecksh
## [117] strawpurchase strawpurchaseh
## [119] tenroundlimit theft
## [121] threedaylimit traffickingbackground
## [123] traffickingprohibited traffickingprohibitedh
## [125] training universal
## [127] universalh universalpermit
## [129] universalpermith violent
## [131] violenth violentpartial
## [133] waiting waitingh
## 134 Levels: age18longgunpossess age18longgunsale ... waitinghOrganized into 14 categories…
unique(siegel_raw$Category)## [1] possession.regulations
## [2] buyer.regulations
## [3] prohibitions.for.high-risk.gun.possession
## [4] ammunition.regulations
## [5] assault.weapons.and.large-capacity.magazines
## [6] background.checks
## [7] child.access.prevention
## [8] concealed.carry.permitting
## [9] dealer.regulations
## [10] domestic.violence
## [11] immunity
## [12] gun.trafficking
## [13] stand.your.ground
## [14] preemption
## 14 Levels: ammunition.regulations ...And 50 sub-categories
unique(siegel_raw$Sub.Category)## [1] Age restrictions
## [2] Alcohol
## [3] Background checks
## [4] Licensing
## [5] Permitting
## [6] Recordkeeping
## [7] Prohibitors
## [8] Assault weapons ban
## [9] Background check records
## [10] Storage
## [11] Campus carry
## [12] Mental Health
## [13] Registration
## [14] Drugs
## [15] Restraining order
## [16] School zones
## [17] Felony
## [18] Fingerprinting
## [19] Gun shows
## [20] Gun violence restraining orders
## [21] Immunity
## [22] Firearm removal
## [23] Inspections
## [24] Junk guns
## [25] Liability
## [26] Safety locks
## [27] Theft reporting
## [28] Large capacity magazine ban
## [29] Misdemeanor crimes
## [30] Background checks - mental health records
## [31] Crime gun identification
## [32] Stand your ground
## [33] Bulk purchase limit
## [34] Open carry
## [35] Personalized gun technology
## [36] Preemption
## [37] Reporting
## [38] Relinquishment of weapons
## [39] Location
## [40] Security
## [41] Stalking
## [42] Background checks - state records
## [43] Straw purchase
## [44] Background checks time limit
## [45] Gun trafficking
## [46] Safety training
## [47] Universal background checks
## [48] Background checks through permits
## [49] Violent Misdemeanor
## [50] Waiting period
## 50 Levels: Age restrictions Alcohol ... Waiting periodScores over time
# source("~scripts/20 - Clean Siegel data.R")
siegelSum <- readRDS("~outputs/20/20_siegelSum.RDS")ggplot(siegelSum,
aes(x = year,
y = score)) +
geom_line() +
facet_wrap(~ state, ncol = 10, scales = "free_x") +
plotTheme() +
labs(title = "Siegel Scores (sum of laws) by ")
Gun Crimes
Number of Cities: 34 Number of States: 29
unique(guns_clean$city)## [1] "Atlanta" "Auburn" "Baltimore"
## [4] "Baton Rouge" "Boston" "Chicago"
## [7] "Cincinnati" "Columbia" "Dallas"
## [10] "Denver" "Detroit" "Gainesville"
## [13] "Hartford" "Indianapolis" "Kansas City"
## [16] "Lincoln" "Little Rock" "Los Angeles"
## [19] "Louisville" "Madison" "Minneapolis"
## [22] "Nashville" "New York" "Phoenix"
## [25] "Portland" "Raleigh" "Sacramento County"
## [28] "Saint Paul" "Salt Lake City" "San Francisco"
## [31] "St Louis County" "Tucson" "Virginia Beach"unique(guns_clean$state)## [1] "Georgia" "Washington" "Maryland" "Louisiana"
## [5] "Massachusetts" "Illinois" "Ohio" "South Carolina"
## [9] "Texas" "Colorado" "Michigan" "Florida"
## [13] "Connecticut" "Indiana" "Missouri" "Nebraska"
## [17] "Arkansas" "California" "Kentucky" "Wisconsin"
## [21] "Minnesota" "Tennessee" "New York" "Arizona"
## [25] "Oregon" "North Carolina" "Utah" "Virginia"Date Range:
- Occurrence Date: 06/19/1922 - 05/01/2020
- Report Date: 10/03/1960 - 05/01/2020
plan(multiprocess)
guns_sample_ls <- future_map(guns_sample_ls,
~ .x %>%
mutate(clean_occur_date = anydate(occurdate), # use built-in formats from anytime package
# correct some incorrectly parsed observations
clean_occur_date = case_when(occurdate == "1" ~ as.Date(NA),
clean_occur_date < as.Date("1900-01-01") ~ as.Date(NA),
is.na(clean_occur_date) &
str_detect(occurdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(occurdate, "%m/%d/%y"),
TRUE ~ clean_occur_date),
clean_report_date = anydate(reportdate),
clean_report_date = case_when(reportdate == "1" ~ as.Date(NA),
clean_report_date < as.Date("1900-01-01") ~ as.Date(NA),
is.na(clean_report_date) &
str_detect(reportdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(reportdate, "%m/%d/%y"),
TRUE ~ clean_report_date)))range(guns_clean$clean_occur_date, na.rm = TRUE)## [1] "1922-06-19" "2020-05-01"range(guns_clean$clean_report_date, na.rm = TRUE)## [1] "1960-10-03" "2020-05-01"Benchmarking results show furrr is fastest for manipulating the data.
benchmark_results <- benchmark("flat" = {
flat <- guns_sample_df %>%
mutate(clean_occur_date = anydate(occurdate), # use built-in formats from anytime package
# correct some incorrectly parsed observations
clean_occur_date = case_when(occurdate == "1" ~ as.Date(NA),
is.na(clean_occur_date) &
str_detect(occurdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(occurdate, "%m/%d/%y"),
TRUE ~ clean_occur_date),
clean_report_date = anydate(reportdate),
clean_report_date = case_when(is.na(clean_report_date) &
str_detect(reportdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(reportdate, "%m/%d/%y"),
TRUE ~ clean_report_date))}, # benchmarking shows the furrr approach is fastest (but still not very fast)
"purrr" = {map(guns_sample_ls,
~ .x %>%
mutate(clean_occur_date = anydate(occurdate), # use built-in formats from anytime package
# correct some incorrectly parsed observations
clean_occur_date = case_when(occurdate == "1" ~ as.Date(NA),
is.na(clean_occur_date) &
str_detect(occurdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(occurdate, "%m/%d/%y"),
TRUE ~ clean_occur_date),
clean_report_date = anydate(reportdate),
clean_report_date = case_when(is.na(clean_report_date) &
str_detect(reportdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(reportdate, "%m/%d/%y"),
TRUE ~ clean_report_date)))},
# plan(multiprocess)
"furrr" = {future_map(guns_sample_ls,
~ .x %>%
mutate(clean_occur_date = anydate(occurdate), # use built-in formats from anytime package
# correct some incorrectly parsed observations
clean_occur_date = case_when(occurdate == "1" ~ as.Date(NA),
is.na(clean_occur_date) &
str_detect(occurdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(occurdate, "%m/%d/%y"),
TRUE ~ clean_occur_date),
clean_report_date = anydate(reportdate),
clean_report_date = case_when(is.na(clean_report_date) &
str_detect(reportdate,
".*\\d+/\\d+/\\d+.*") ~ # e.g "12/3/15", "12/3/15 1600"
as.Date(reportdate, "%m/%d/%y"),
TRUE ~ clean_report_date)))},
replications = 1
)Plots
Data:
# source("~scripts/21 - Clean crime data.R")
guns_list_shp <- readRDS("~outputs/20/21_guns_list_shp.rds")Source script:
source("~scripts/31 - Explore crime data.R")Crime counts by city
gunIncident_summary %>%
arrange(desc(prop)) %>%
kable() %>%
kable_styling()| city | gun_count | all_crimes_count | prop |
|---|---|---|---|
| Madison | 3867 | 12991 | 0.2976676 |
| Los Angeles | 280066 | 2114091 | 0.1324758 |
| St Louis County | 6844 | 65111 | 0.1051128 |
| Baltimore | 30264 | 300336 | 0.1007671 |
| Atlanta | 32802 | 349277 | 0.0939140 |
| Nashville | 90126 | 1088679 | 0.0827847 |
| Chicago | 535385 | 7105053 | 0.0753527 |
| Baton Rouge | 26374 | 456033 | 0.0578335 |
| Indianapolis | 34232 | 750728 | 0.0455984 |
| Kansas City | 89619 | 2143395 | 0.0418117 |
| Hartford | 25551 | 668332 | 0.0382310 |
| Saint Paul | 9050 | 248359 | 0.0364392 |
| Sacramento County | 12962 | 362005 | 0.0358061 |
| Dallas | 38298 | 1089765 | 0.0351434 |
| Columbia | 793 | 25459 | 0.0311481 |
| Minneapolis | 6654 | 217760 | 0.0305566 |
| San Francisco | 71555 | 2546472 | 0.0280997 |
| Virginia Beach | 3053 | 124693 | 0.0244841 |
| New York | 162424 | 6847944 | 0.0237187 |
| Louisville | 29540 | 1330690 | 0.0221990 |
| Denver | 9819 | 563795 | 0.0174159 |
| Lincoln | 4192 | 241098 | 0.0173871 |
| Boston | 12769 | 746868 | 0.0170967 |
| Portland | 2949 | 214817 | 0.0137280 |
| Tucson | 22545 | 1828868 | 0.0123273 |
| Raleigh | 7230 | 719797 | 0.0100445 |
| Cincinnati | 3507 | 395676 | 0.0088633 |
| Gainesville | 1319 | 157253 | 0.0083878 |
| Auburn | 182 | 22383 | 0.0081312 |
| Detroit | 5684 | 872947 | 0.0065113 |
| Salt Lake City | 4254 | 793509 | 0.0053610 |
| Little Rock | 215 | 86909 | 0.0024739 |
| Phoenix | 594 | 294292 | 0.0020184 |
gunIncidentsByCityPlot
How many NA observations?
na_coords_summary <- map(guns_list,
~ sum(is.na(.x$lon) | is.na(.x$lat)) /
nrow(.x)) %>%
bind_rows() %>%
gather(key = "City",
value = "pct_NA")na_coords_summary %>%
arrange(desc(pct_NA)) %>%
kable() %>%
kable_styling()| City | pct_NA |
|---|---|
| Portland | 0.1254845 |
| Boston | 0.0574702 |
| Kansas City | 0.0491456 |
| Baton Rouge | 0.0464759 |
| Dallas | 0.0391989 |
| Sacramento County | 0.0353341 |
| New York | 0.0317222 |
| Raleigh | 0.0298574 |
| St Louis County | 0.0246932 |
| Lincoln | 0.0238797 |
| Cincinnati | 0.0208155 |
| Auburn | 0.0200000 |
| Nashville | 0.0186494 |
| Little Rock | 0.0186047 |
| Salt Lake City | 0.0179098 |
| Philadelphia | 0.0106018 |
| Gainesville | 0.0068234 |
| Tucson | 0.0066634 |
| Louisville | 0.0054160 |
| Chicago | 0.0050338 |
| Baltimore | 0.0047251 |
| Minneapolis | 0.0025467 |
| Saint Paul | 0.0022080 |
| San Francisco | 0.0018446 |
| Detroit | 0.0016895 |
| Madison | 0.0012930 |
| Indianapolis | 0.0009932 |
| Virginia Beach | 0.0009826 |
| Denver | 0.0008099 |
| Los Angeles | 0.0004548 |
| Atlanta | 0.0000000 |
| Columbia | 0.0000000 |
| Hartford | 0.0000000 |
| Phoenix | 0.0000000 |
All Cities
Maps of each city
Atlanta
Auburn
Baltimore
Baton Rouge
Boston
Chicago
Cincinnati
Columbia
Dallas
Denver
Detroit
Gainesville
Hartford
Indianapolis
Kansas City
Lincoln
Little Rock
Los Angeles
Louisville
Madison
Minneapolis
Nashville
New York
Philadelphia
Phoenix
Portland
Raleigh
Sacramento County
Saint Paul
Salt Lake City
San Francisco
St Louis County
Tucson
Virginia Beach
Moran’s I
What is Moran’s I?
Inferential statistic ranging from -1 to 1 that describes the level of dispersal/clustering evident in spatial data. Associated with a p-value that provides the statistical significance of the estimate.
Moran’s I range
That assumes a uniform intensity to all values. We need to choose some value for determining an “intensity” for the areas. For now, I went with “gun crimes per 100 people”. Is there another metric that might be better?
Read in the study area geographies w/ crimes per 100, raw crime counts, and population per tract.
Another question is the geographic scope we look at. Some choices. I looked at Census-designated place for now, because those generally align with published city borders.
A concave or convex hull may better fit the data, though.
- Tracts selected via:
- Concave hull of gun crime observations
- Convex hull of gun crime observations
- All tracts in any county with at least 1 gun crime observation
- Census-designated places matching the city/county name
- Block Groups selected via the same criteria
# source("~scripts/33 - Aggregate crimes and census geographies.R")
tracts_crimeCounts <- readRDS("~outputs/30/33_tracts_crimeCounts.rds")
BGs_crimeCounts <- readRDS("~outputs/30/33_BGs_crimeCounts.rds")tmap::tmap_mode("view")
tmap::qtm(tracts_crimeCounts$byCaveHull$`San Francisco`, title = "Concave hull of crimes")tmap::qtm(tracts_crimeCounts$byVexHull$`San Francisco`, title = "Convex hull of crimes")tmap::qtm(tracts_crimeCounts$byCounty$`San Francisco`, title = "County")tmap::qtm(tracts_crimeCounts$byPlace$`San Francisco`, title = "Census-designated place")# source("~scripts/34 - Calculate Moran's I.R")
tracts_I <- readRDS("~outputs/30/34_tracts_I.rds")
BGs_I <- readRDS("~outputs/30/34_BGs_I.rds")
tracts_pop_I <- readRDS("~outputs/30/34_tracts_pop_I.rds")
BGs_pop_I <- readRDS("~outputs/30/34_BGs_pop_I.rds")
tracts_per100_I <- readRDS("~outputs/30/34_tracts_per100_I.rds")
BGs_per100_I <- readRDS("~outputs/30/34_BGs_per100_I.rds")# crime Moran's I tract
I_crime_tr <- map_dfr(tracts_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(crime_I = `Moran I statistic`)
p_crime_tr <- map_dfr(tracts_I$byPlace,
~ data.frame(pval_crime = .x$p.value),
.id = "City") %>%
mutate(geo = "Tract",
pval_crime = ifelse(pval_crime < 0.01, "< 0.01", "> 0.01"))
# crime Moran's I block group
I_crime_BG <- map_dfr(BGs_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(crime_I = `Moran I statistic`)
p_crime_BG <- map_dfr(BGs_I$byPlace,
~ data.frame(pval_crime = .x$p.value),
.id = "City") %>%
mutate(geo = "Block Group",
pval_crime = ifelse(pval_crime < 0.01, "< 0.01", "> 0.01"))
# pop Moran's I tract
I_pop_tr <- map_dfr(tracts_pop_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(pop_I = `Moran I statistic`)
p_pop_tr <- map_dfr(tracts_pop_I$byPlace,
~ data.frame(pval_pop = .x$p.value),
.id = "City") %>%
mutate(geo = "Tract",
pval_pop = ifelse(pval_pop < 0.01, "< 0.01", "> 0.01"))
# pop Moran's I block group
I_pop_BG <- map_dfr(BGs_pop_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(pop_I = `Moran I statistic`)
p_pop_BG <- map_dfr(BGs_pop_I$byPlace,
~ data.frame(pval_pop = .x$p.value),
.id = "City") %>%
mutate(geo = "Block Group",
pval_pop = ifelse(pval_pop < 0.01, "< 0.01", "> 0.01"))
# per100 Moran's I tract
I_per100_tr <- map_dfr(tracts_per100_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(per100_I = `Moran I statistic`)
p_per100_tr <- map_dfr(tracts_per100_I$byPlace,
~ data.frame(pval_per100 = .x$p.value),
.id = "City") %>%
mutate(geo = "Tract",
pval_per100 = ifelse(pval_per100 < 0.01, "< 0.01", "> 0.01"))
# per100 Moran's I block group
I_per100_BG <- map_dfr(BGs_per100_I$byPlace,
~ .x$estimate[1],
.id = "City") %>%
rename(per100_I = `Moran I statistic`)
p_per100_BG <- map_dfr(BGs_per100_I$byPlace,
~ data.frame(pval_per100 = .x$p.value),
.id = "City") %>%
mutate(geo = "Block Group",
pval_per100 = ifelse(pval_per100 < 0.01, "< 0.01", "> 0.01"))
I_crime_tmp <- left_join(I_crime_tr, p_crime_tr,
by = "City") %>%
rbind(left_join(I_crime_BG, p_crime_BG,
by = "City"))
I_pop_tmp <- left_join(I_pop_tr, p_pop_tr,
by = "City") %>%
rbind(left_join(I_pop_BG, p_pop_BG,
by = "City"))
I_per100_tmp <- left_join(I_per100_tr, p_per100_tr,
by = "City") %>%
rbind(left_join(I_per100_BG, p_per100_BG,
by = "City"))
I_tmp <- left_join(I_crime_tmp, I_pop_tmp, by = c("City", "geo")) %>%
left_join(I_per100_tmp, by = c("City", "geo"))
I_wide <- I_tmp %>%
pivot_wider(names_from = "geo",
values_from = c("crime_I", "pop_I", "pval_crime", "pval_pop", "per100_I", "pval_per100")) %>%
dplyr::select(City,
per100_tr = per100_I_Tract,
per100_tr_p = pval_per100_Tract,
crime_tr = crime_I_Tract,
crime_tr_p = pval_crime_Tract,
pop_tr = pop_I_Tract,
pop_tr_p = pval_pop_Tract,
per100_BG = `per100_I_Block Group`,
per100_BG_p = `pval_per100_Block Group`,
crime_BG = `crime_I_Block Group`,
crime_BG_p = `pval_crime_Block Group`,
pop_BG = `pop_I_Block Group`,
pop_BG_p = `pval_pop_Block Group`)Below is a table showing Moran’s I for crimes per 100 for census tracts and block groups for each study area selected by the relevant Census-designated place, raw crime counts, and population. We see a big range from 0 (essentially random gun crime distribution relative to population) to very high
Questions:
- Weighting by population is important. Is Moran’s I of crimes per 100 the best way?
- How might we interpret a city that has a large difference in Moran’s I between tracts and block groups (e.g. Kansas City)?
I_wide %>%
mutate(crime_tr_p = cell_spec(crime_tr_p,
"html",
background = ifelse(str_detect(crime_tr_p, ">"),
"red",
"white")),
pop_tr_p = cell_spec(pop_tr_p,
"html",
background = ifelse(str_detect(pop_tr_p, ">"),
"red",
"white")),
per100_tr_p = cell_spec(per100_tr_p,
"html",
background = ifelse(str_detect(per100_tr_p, ">"),
"red",
"white")),
crime_BG_p = cell_spec(crime_BG_p,
"html",
background = ifelse(str_detect(crime_BG_p, ">"),
"red",
"white")),
pop_BG_p = cell_spec(pop_BG_p,
"html",
background = ifelse(str_detect(pop_BG_p, ">"),
"red",
"white")),
per100_BG_p = cell_spec(per100_BG_p,
"html",
background = ifelse(str_detect(per100_BG_p, ">"),
"red",
"white"))) %>%
arrange(desc(per100_tr)) %>%
kable(format = "html",
escape = FALSE,
digits = 2,
caption = "Moran's I for Crimes per 100 people, Crime Count, and Population by Tract and Block Group") %>%
kable_styling(bootstrap_options = "striped")| City | per100_tr | per100_tr_p | crime_tr | crime_tr_p | pop_tr | pop_tr_p | per100_BG | per100_BG_p | crime_BG | crime_BG_p | pop_BG | pop_BG_p |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Hartford | 0.76 | < 0.01 | 0.70 | < 0.01 | 0.17 | < 0.01 | 0.76 | < 0.01 | 0.73 | < 0.01 | 0.13 | < 0.01 |
| Chicago | 0.75 | < 0.01 | 0.59 | < 0.01 | 0.31 | < 0.01 | 0.53 | < 0.01 | 0.56 | < 0.01 | 0.22 | < 0.01 |
| Louisville | 0.74 | < 0.01 | 0.61 | < 0.01 | 0.24 | < 0.01 | 0.58 | < 0.01 | 0.59 | < 0.01 | 0.22 | < 0.01 |
| Indianapolis | 0.73 | < 0.01 | 0.57 | < 0.01 | 0.37 | < 0.01 | 0.67 | < 0.01 | 0.51 | < 0.01 | 0.35 | < 0.01 |
| Raleigh | 0.66 | < 0.01 | 0.69 | < 0.01 | 0.36 | < 0.01 | 0.63 | < 0.01 | 0.57 | < 0.01 | 0.27 | < 0.01 |
| Saint Paul | 0.62 | < 0.01 | 0.49 | < 0.01 | 0.18 | < 0.01 | 0.58 | < 0.01 | 0.54 | < 0.01 | 0.17 | < 0.01 |
| Minneapolis | 0.58 | < 0.01 | 0.47 | < 0.01 | 0.13 | < 0.01 | 0.55 | < 0.01 | 0.42 | < 0.01 | 0.23 | < 0.01 |
| Baton Rouge | 0.57 | < 0.01 | 0.40 | < 0.01 | 0.27 | < 0.01 | 0.49 | < 0.01 | 0.50 | < 0.01 | 0.24 | < 0.01 |
| Little Rock | 0.57 | < 0.01 | 0.54 | < 0.01 | 0.13 | > 0.01 | 0.35 | < 0.01 | 0.40 | < 0.01 | 0.15 | < 0.01 |
| Baltimore | 0.55 | < 0.01 | 0.36 | < 0.01 | 0.24 | < 0.01 | 0.03 | < 0.01 | 0.38 | < 0.01 | 0.17 | < 0.01 |
| Nashville | 0.51 | < 0.01 | 0.49 | < 0.01 | 0.28 | < 0.01 | 0.32 | < 0.01 | 0.37 | < 0.01 | 0.38 | < 0.01 |
| Tucson | 0.50 | < 0.01 | 0.55 | < 0.01 | 0.07 | > 0.01 | 0.36 | < 0.01 | 0.42 | < 0.01 | 0.29 | < 0.01 |
| St Louis County | 0.50 | < 0.01 | 0.49 | < 0.01 | 0.19 | < 0.01 | 0.50 | < 0.01 | 0.56 | < 0.01 | 0.28 | < 0.01 |
| Columbia | 0.50 | < 0.01 | 0.37 | < 0.01 | 0.20 | < 0.01 | 0.21 | < 0.01 | 0.31 | < 0.01 | 0.26 | < 0.01 |
| Atlanta | 0.49 | < 0.01 | 0.43 | < 0.01 | 0.27 | < 0.01 | 0.43 | < 0.01 | 0.41 | < 0.01 | 0.19 | < 0.01 |
| Salt Lake City | 0.46 | < 0.01 | 0.17 | < 0.01 | 0.21 | < 0.01 | 0.50 | < 0.01 | 0.10 | < 0.01 | 0.32 | < 0.01 |
| Philadelphia | 0.37 | < 0.01 | 0.64 | < 0.01 | 0.24 | < 0.01 | 0.43 | < 0.01 | 0.46 | < 0.01 | 0.16 | < 0.01 |
| Gainesville | 0.36 | < 0.01 | 0.38 | < 0.01 | 0.03 | > 0.01 | 0.29 | < 0.01 | 0.34 | < 0.01 | 0.01 | > 0.01 |
| Detroit | 0.33 | < 0.01 | 0.41 | < 0.01 | 0.33 | < 0.01 | 0.05 | < 0.01 | 0.36 | < 0.01 | 0.27 | < 0.01 |
| Portland | 0.32 | < 0.01 | 0.33 | < 0.01 | 0.26 | < 0.01 | 0.37 | < 0.01 | 0.38 | < 0.01 | 0.33 | < 0.01 |
| Cincinnati | 0.29 | < 0.01 | 0.17 | < 0.01 | 0.28 | < 0.01 | 0.39 | < 0.01 | 0.15 | < 0.01 | 0.14 | < 0.01 |
| Madison | 0.29 | < 0.01 | 0.26 | < 0.01 | 0.27 | < 0.01 | 0.32 | < 0.01 | 0.28 | < 0.01 | 0.14 | < 0.01 |
| Phoenix | 0.25 | < 0.01 | 0.32 | < 0.01 | 0.18 | < 0.01 | 0.17 | < 0.01 | 0.24 | < 0.01 | 0.26 | < 0.01 |
| Kansas City | 0.24 | < 0.01 | 0.36 | < 0.01 | 0.41 | < 0.01 | 0.20 | < 0.01 | 0.16 | < 0.01 | 0.41 | < 0.01 |
| Boston | 0.20 | < 0.01 | 0.59 | < 0.01 | 0.10 | < 0.01 | 0.22 | < 0.01 | 0.52 | < 0.01 | 0.05 | > 0.01 |
| Auburn | 0.19 | < 0.01 | 0.22 | < 0.01 | -0.22 | > 0.01 | 0.35 | < 0.01 | 0.37 | < 0.01 | 0.00 | > 0.01 |
| Denver | 0.15 | < 0.01 | 0.43 | < 0.01 | 0.15 | < 0.01 | 0.36 | < 0.01 | 0.33 | < 0.01 | 0.11 | < 0.01 |
| Lincoln | 0.10 | < 0.01 | 0.51 | < 0.01 | -0.03 | > 0.01 | 0.24 | < 0.01 | 0.40 | < 0.01 | 0.35 | < 0.01 |
| Sacramento County | 0.10 | < 0.01 | 0.57 | < 0.01 | 0.13 | < 0.01 | 0.26 | < 0.01 | 0.57 | < 0.01 | 0.17 | < 0.01 |
| Virginia Beach | 0.08 | < 0.01 | 0.24 | < 0.01 | 0.16 | < 0.01 | 0.25 | < 0.01 | 0.25 | < 0.01 | 0.15 | < 0.01 |
| Dallas | 0.00 | > 0.01 | 0.44 | < 0.01 | 0.30 | < 0.01 | 0.01 | > 0.01 | 0.27 | < 0.01 | 0.26 | < 0.01 |
| New York | 0.00 | > 0.01 | 0.45 | < 0.01 | 0.30 | < 0.01 | 0.00 | > 0.01 | 0.28 | < 0.01 | 0.15 | < 0.01 |
| Los Angeles | -0.01 | > 0.01 | 0.47 | < 0.01 | 0.11 | < 0.01 | -0.01 | > 0.01 | 0.40 | < 0.01 | 0.17 | < 0.01 |
| San Francisco | -0.01 | > 0.01 | 0.51 | < 0.01 | 0.11 | < 0.01 | 0.08 | < 0.01 | 0.60 | < 0.01 | 0.05 | < 0.01 |
Crimes by census tract for each city
Atlanta
Auburn
Baltimore
Baton Rouge
Boston
Chicago
Cincinnati
Columbia
Dallas
Denver
Detroit
Gainesville
Hartford
Indianapolis
Kansas City
Lincoln
Little Rock
Los Angeles
Louisville
Madison
Minneapolis
Nashville
New York
Philadelphia
Phoenix
Portland
Raleigh
Sacramento County
Saint Paul
Salt Lake City
San Francisco
St Louis County
Tucson
Virginia Beach
